Literature DB >> 12169534

Inferring sub-cellular localization through automated lexical analysis.

Rajesh Nair1, Burkhard Rost.   

Abstract

MOTIVATION: The SWISS-PROT sequence database contains keywords of functional annotations for many proteins. In contrast, information about the sub-cellular localization is available for only a few proteins. Experts can often infer localization from keywords describing protein function. We developed LOCkey, a fully automated method for lexical analysis of SWISS-PROT keywords that assigns sub-cellular localization. With the rapid growth in sequence data, the biochemical characterisation of sequences has been falling behind. Our method may be a useful tool for supplementing functional information already automatically available.
RESULTS: The method reached a level of more than 82% accuracy in a full cross-validation test. Due to a lack of functional annotations, we could infer localization for fewer than half of all proteins in SWISS-PROT. We applied LOCkey to annotate five entirely sequenced proteomes, namely Saccharomyces cerevisiae (yeast), Caenorhabditis elegans (worm), Drosophila melanogaster (fly), Arabidopsis thaliana (plant) and a subset of all human proteins. LOCkey found about 8000 new annotations of sub-cellular localization for these eukaryotes.

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Year:  2002        PMID: 12169534     DOI: 10.1093/bioinformatics/18.suppl_1.s78

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  20 in total

1.  LOC3D: annotate sub-cellular localization for protein structures.

Authors:  Rajesh Nair; Burkhard Rost
Journal:  Nucleic Acids Res       Date:  2003-07-01       Impact factor: 16.971

2.  The PredictProtein server.

Authors:  Burkhard Rost; Guy Yachdav; Jinfeng Liu
Journal:  Nucleic Acids Res       Date:  2004-07-01       Impact factor: 16.971

3.  LOCnet and LOCtarget: sub-cellular localization for structural genomics targets.

Authors:  Rajesh Nair; Burkhard Rost
Journal:  Nucleic Acids Res       Date:  2004-07-01       Impact factor: 16.971

4.  Go molecular function terms are predictive of subcellular localization.

Authors:  Z Lu; L Hunter
Journal:  Pac Symp Biocomput       Date:  2005

5.  Predicting the subcellular localization of human proteins using machine learning and exploratory data analysis.

Authors:  George K Acquaah-Mensah; Sonia M Leach; Chittibabu Guda
Journal:  Genomics Proteomics Bioinformatics       Date:  2006-05       Impact factor: 7.691

6.  Going from where to why--interpretable prediction of protein subcellular localization.

Authors:  Sebastian Briesemeister; Jörg Rahnenführer; Oliver Kohlbacher
Journal:  Bioinformatics       Date:  2010-03-17       Impact factor: 6.937

7.  TESTLoc: protein subcellular localization prediction from EST data.

Authors:  Yao-Qing Shen; Gertraud Burger
Journal:  BMC Bioinformatics       Date:  2010-11-15       Impact factor: 3.169

Review 8.  Computational and experimental approaches to chart the Escherichia coli cell-envelope-associated proteome and interactome.

Authors:  Juan Javier Díaz-Mejía; Mohan Babu; Andrew Emili
Journal:  FEMS Microbiol Rev       Date:  2008-11-27       Impact factor: 16.408

9.  MultiLoc2: integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction.

Authors:  Torsten Blum; Sebastian Briesemeister; Oliver Kohlbacher
Journal:  BMC Bioinformatics       Date:  2009-09-01       Impact factor: 3.169

10.  LocTree2 predicts localization for all domains of life.

Authors:  Tatyana Goldberg; Tobias Hamp; Burkhard Rost
Journal:  Bioinformatics       Date:  2012-09-15       Impact factor: 6.937

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